Boukhechba Mehdi, Chow Philip, Fua Karl, Teachman Bethany A, Barnes Laura E
Systems and Information Engineering Department, School of Engineering and Applied Science, University of Virginia, Charlottesville, VA, United States.
Department of Psychology, University of Virginia, Charlottesville, VA, United States.
JMIR Ment Health. 2018 Jul 4;5(3):e10101. doi: 10.2196/10101.
Social anxiety is highly prevalent among college students. Current methodologies for detecting symptoms are based on client self-report in traditional clinical settings. Self-report is subject to recall bias, while visiting a clinic requires a high level of motivation. Assessment methods that use passively collected data hold promise for detecting social anxiety symptoms and supplementing self-report measures. Continuously collected location data may provide a fine-grained and ecologically valid way to assess social anxiety in situ.
The objective of our study was to examine the feasibility of leveraging noninvasive mobile sensing technology to passively assess college students' social anxiety levels. Specifically, we explored the different relationships between mobility and social anxiety to build a predictive model that assessed social anxiety from passively generated Global Positioning System (GPS) data.
We recruited 228 undergraduate participants from a Southeast American university. Social anxiety symptoms were assessed using self-report instruments at a baseline laboratory session. An app installed on participants' personal mobile phones passively sensed data from the GPS sensor for 2 weeks. The proposed framework supports longitudinal, dynamic tracking of college students to evaluate the relationship between their social anxiety and movement patterns in the college campus environment. We first extracted the following mobility features: (1) cumulative staying time at each different location, (2) the distribution of visits over time, (3) the entropy of locations, and (4) the frequency of transitions between locations. Next, we studied the correlation between these features and participants' social anxiety scores to enhance the understanding of how students' social anxiety levels are associated with their mobility. Finally, we used a neural network-based prediction method to predict social anxiety symptoms from the extracted daily mobility features.
Several mobility features correlated with social anxiety levels. Location entropy was negatively associated with social anxiety (during weekdays, r=-0.67; and during weekends, r=-0.51). More (vs less) socially anxious students were found to avoid public areas and engage in less leisure activities during evenings and weekends, choosing instead to spend more time at home after school (4 pm-12 am). Our prediction method based on extracted mobility features from GPS trajectories successfully classified participants as high or low socially anxious with an accuracy of 85% and predicted their social anxiety score (on a scale of 0-80) with a root-mean-square error of 7.06.
Results indicate that extracting and analyzing mobility features may help to reveal how social anxiety symptoms manifest in the daily lives of college students. Given the ubiquity of mobile phones in our society, understanding how to leverage passively sensed data has strong potential to address the growing needs for mental health monitoring and treatment.
社交焦虑在大学生中极为普遍。当前检测症状的方法基于传统临床环境中的患者自我报告。自我报告容易受到回忆偏差的影响，而前往诊所则需要高度的积极性。使用被动收集数据的评估方法有望检测社交焦虑症状并补充自我报告测量。持续收集的位置数据可能提供一种细粒度且生态有效的原位评估社交焦虑的方法。
我们研究的目的是检验利用非侵入性移动传感技术被动评估大学生社交焦虑水平的可行性。具体而言，我们探索了移动性与社交焦虑之间的不同关系，以建立一个从被动生成的全球定位系统（GPS）数据评估社交焦虑的预测模型。
我们从美国东南部一所大学招募了228名本科参与者。在基线实验室环节使用自我报告工具评估社交焦虑症状。安装在参与者个人手机上的应用程序被动感知来自GPS传感器的数据，为期2周。所提出的框架支持对大学生进行纵向、动态跟踪，以评估他们的社交焦虑与大学校园环境中运动模式之间的关系。我们首先提取了以下移动性特征：（1）在每个不同位置的累计停留时间，（2）随时间的访问分布，（3）位置的熵，以及（4）位置之间的转换频率。接下来，我们研究了这些特征与参与者社交焦虑得分之间的相关性，以加深对学生社交焦虑水平如何与其移动性相关联的理解。最后，我们使用基于神经网络的预测方法从提取的每日移动性特征预测社交焦虑症状。
几个移动性特征与社交焦虑水平相关。位置熵与社交焦虑呈负相关（工作日期间，r = -0.67；周末期间，r = -0.51）。发现社交焦虑程度较高（相对于较低）的学生在晚上和周末会避开公共场所并减少休闲活动，而是选择放学后（下午4点至凌晨12点）更多时间待在家里。我们基于从GPS轨迹提取的移动性特征的预测方法成功将参与者分类为社交焦虑程度高或低，准确率为85%，并以7.06的均方根误差预测他们的社交焦虑得分（范围为0 - 80）。
结果表明，提取和分析移动性特征可能有助于揭示社交焦虑症状在大学生日常生活中的表现方式。鉴于手机在我们社会中的普及，了解如何利用被动感知的数据具有强大的潜力来满足心理健康监测和治疗日益增长的需求。
